GPGPU-based Highly Parallelized 3D Node Localization for Real-Time 3D Model Reproduction

Abstract

This paper proposes a highly parallelized 3D node localization method based on cross-entropy method for the 3D modeling system. Cross-entropy localization statistically estimates node positions from node-to-node distance information by sampling, and each sample evaluation and internal computation of objective function can be processed in parallel. Experimental results show our GPGPU-based implementation achieved 5,163x and 61.5x speed up compared to a single processor and 80-processor implementations. In addition, for enhancing model reproduction accuracy, this work introduces a penalty function to mitigate flip ambiguity.